Broadband beamforming via frequency invariance transformation and PARAFAC decomposition

For the next generation communications, a high data-rate scenario is expected not only due to the increasing amount of mobile subscribers, but also due to the impact of technologies such as the Internet of Things (IoT), Vehicular Ad Hoc Networks (VANETs) and Virtual Reality (VR). One of the key technologies to allow for a better exploitation of the scarce spectrum is the incorporation of antenna arrays into communication devices. In that sense, beamforming is an array processing tool that provides spatial separation of multiple sources sharing the same spectrum band. In this work, we propose a framework composed of a bank of frequency invariant beamformers (FIB) and an adaptive parallel factor analysis (PARAFAC) decomposition instead of the state-of-the art independent component analysis (ICA). The original PARAFAC adaptation is modified for scenarios where the signals are time-correlated (non-white) and the a pseudo-inversion step is added for an increased accuracy. Our proposed framework outperforms the state-of-the-art methods in terms of accuracy and convergence.